---
title: "Nutzungsstatistiken 2020"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
 #   self_contained: false 
    # https://stackoverflow.com/questions/54643852/how-to-reduce-the-size-of-flexdashboards
---

 


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
                      message = FALSE,
                      warning = FALSE,
                      options(scipen = 999))

library(flexdashboard)
library(readxl)
library(tidyverse)
library(lubridate)
library(gt)
library(janitor)
library(scales)
#library(DT)
library(plotly)
library(gameofthrones)

# Ressources:
# https://towardsdatascience.com/building-an-hr-dashboard-in-r-using-flexdashboard-76d14ed3f32
# https://rmarkdown.rstudio.com/flexdashboard/using.html

```

Dash 2020 {data-icon="fa-globe"}
===================================== 

Row {data-width=150, data-height=200}
-----------------------------------------------------------------------

### Suchen in Primo 
(im Vergleich zum Vorjahr 2019)

```{r}
articles <- "+16 %"
valueBox(articles,
         icon = "fa-search-plus",
         color = "#36AFAEFF",
         href = "#primo")
```

### Besuche der Website 
(im Vergleich zum Vorjahr 2019)

```{r}
comments <- "+28 %"
valueBox(comments,
         icon = "fa-user-check",
         color = "#36AFAEFF",
         href = "#website")
```

### Ausleihe von Büchern 
(im Vergleich zum Vorjahr 2019)

```{r}
spam <- "-50 %"
valueBox(spam, 
         icon = "fa-book",
         color = "#747374FF", 
         href="#ausleihen")
```

```{r eval=FALSE}
### 
#Die Zahl bezieht sich auf die prozentuale Veränderung zum Vorjahr 2019.
```


Row {data-width=150, data-height=200}
-----------------------------------------------------------------------

### Downloads aus E-Books von Springer 
(im Vergleich zum Vorjahr 2019)

```{r}
articles <- "+13 %"
valueBox(articles,
         icon = "fa-search-plus",
         color = "#36AFAEFF",
         href = "#primo")
```

### Downloads aus E-Journals von Springer 
(im Vergleich zum Vorjahr 2019)

```{r}
articles <- "+25 %"
valueBox(articles,
         icon = "fa-search-plus",
         color = "#36AFAEFF",
         href = "#primo")
```


Row 
-----------------------------------------------------------------------


Primo {data-orientation=columns}
===================================== 

Column {data-width=400, data-height=400}
-----------------------------------------------------------------------

### Primo A

```{r}
Primo_Stat_Auswertungen <-
  read_excel(
    "T:/Statistik/ALMA_ART/Primo_Stat_Auswertungen.xlsx",
    sheet = "R_1",
    col_types = c("date",
                  "text", "numeric")
  )
```

```{r}
Primo <- Primo_Stat_Auswertungen %>%
  mutate(Year = year(Date)) %>%
  group_by(Year, Action) %>%
  summarise(Value = sum(Value))
```

```{r}
Primo_3 <- Primo %>%
  summarise(Search_Total = sum(Value)) %>%
  mutate(Percent_Change = (Search_Total / (lag(Search_Total)) - 1))
```


```{r}
pal <- got(2, option = "Jon_Snow", direction = 1)

p1 <- ggplot(Primo, aes(x = Year, y = Value)) +
  geom_col(aes(fill = Action), position = "dodge") +
  theme_classic() +
  labs(
    title = "Primo: Anzahl Suchen von 2018 bis 2020",
    subtitle = "",
    fill = "Suchen",
    y = "",
    x = ""
  ) +
  scale_fill_manual(values = pal) +
  theme(legend.position = "bottom") 

ggplotly(p1) %>%
  layout(legend = list(orientation = "h", y = -0.1))

#+
 # facet_wrap(~Date)
  
```



Column {data-width=200, data-height=200}  
-----------------------------------------------------------------------

### Primo B


```{r}
Primo_2 <- Primo %>%
  spread(key = Year, value = Value) %>%
  adorn_totals("row")
```


```{r}
gt(Primo_3) %>%
  tab_header(title = md("Suchen in Primo"))
```



Website {data-orientation=columns}
===================================== 

Column
-----------------------------------------------------------------------

### Chart A

```{r}
Website_Besuche <- read_excel(
  "T:/Statistik/ALMA_ART/Website.xlsx",
  sheet = "Besuche",
  col_types = c("numeric",
                "text", "numeric")
)
```

```{r}
Website_Besuche_2 <- Website_Besuche %>%
  group_by(Action) %>%
  #  filter(Action == "Besuche") %>%
  mutate(Percent_Change = (Value / (lag(Value)) - 1))
```


```{r}
pal <- got(2, option = "Jon_Snow", direction = 1)

p1 <- ggplot(Website_Besuche, aes(x = Date, y = Value)) +
  geom_col(aes(fill = Action), position = "dodge") +
  theme_classic() +
  scale_fill_manual(values = pal) +
  labs(title = "Website: Besuche und Seitenansichten 2018 bis 2020",
       fill = "",
       y = "",
       x = "") 

ggplotly(p1)
```

### Chart B

```{r}
Website_OS <- read_excel(
  "T:/Statistik/ALMA_ART/Website.xlsx",
  sheet = "Desktop",
  col_types = c("text", "text", "numeric")
)
```


```{r}
pal <- got(3, option = "Jon_Snow", direction = -1)

p1 <- Website_OS %>%
  filter(date == "2020") %>%
  plot_ly(marker = list(colors = pal)) %>%
  add_pie(labels = Website_OS$action,
          values = Website_OS$value,
          hole = 0.6) %>%
  layout(title = "Website: Verwendung Betriebssysteme 2020")
p1
```



Column
-----------------------------------------------------------------------


### Chart C

```{r}
Website_Users <- read_excel(
  "T:/Statistik/ALMA_ART/Website.xlsx",
  sheet = "Users",
  col_types = c("date", "numeric", "numeric")
)

pal <- got(3, option = "Jon_Snow", direction = 1)
```

```{r}
Website_Users_2 <- Website_Users %>%
  mutate(month = as.factor(month(date, label = T, abb = T))) %>%
  mutate(year = as.factor(year(date)))

p2 <-
  ggplot(Website_Users_2, aes(x = month, y = visits, color = year)) +
  geom_point(size = 2) +
  geom_line(aes(x = month, y = visits, group = year), size = 1) +
  theme(axis.text.x = element_text(angle = 90)) +
  #  scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_color_manual(values = pal) +
  labs(
    title = "Website: Besuche 2018 bis 2020",
    y = "Besuche",
    x = "",
    color = ""
  )

ggplotly(p2)
```

### Chart D




```{r}
Website_Users_2 <- Website_Users %>%
  mutate(month = as.factor(month(date, label = T, abb = T))) %>%
  mutate(year = as.factor(year(date)))
#%>%
#  filter(date >= "2019-01-01")


p1 <-
  ggplot(Website_Users_2, aes(x = month, y = unique_users, color = year)) +
  geom_point(size = 2) +
  geom_line(aes(x = month, y = unique_users, group = year), size = 1) +
  theme(axis.text.x = element_text(angle = 90)) +
  #  scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_color_manual(values = pal) +
  labs(
    title = "Website: Unique Users 2018 bis 2020",
    y = "Unique Users",
    x = "",
    color = ""
  )

ggplotly(p1)
```



Ausleihen {data-orientation=columns}
===================================== 

Column {data-width=400, data-height=400}
-----------------------------------------------------------------------

### Ausleihen

```{r}
Ausleihen <- read_excel(
  "T:/Statistik/ALMA_ART/Ausleihen.xlsx",
  sheet = "Ausleihen_R",
  col_types = c("text",
                "numeric", "numeric", "numeric",
                "numeric"),
  na = "NA"
)
```

```{r}
pal <- got(12, option = "Jon_Snow", direction = -1)

p1 <-
  ggplot(Ausleihen, aes(x = Jahr, y = Ausleihen_u_Verl)) +
  geom_col(fill = pal[1]) +
  theme(axis.text.x = element_text(angle = 90)) +
  #  scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(
    title = "Ausleihen alle Standorte 2007 bis 2020",
    y = "Ausleihen",
    x = "",
    color = ""
  )

ggplotly(p1)
```

```{r}
Ausleihen %>%
  mutate(Percent_Change = (Ausleihen_u_Verl / (lag(Ausleihen_u_Verl)) -
                             1))
```